Computation and Language - ARC Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs

Hey PaperLedge crew, Ernis here! Get ready to dive into some fascinating research that could change the way we understand AI and information. Today, we're cracking open a paper that looks at how well Large Language Models, or LLMs – think of them as super-smart AI text generators – are at summarizing complex documents, especially when those documents have a strong argumentative structure. Imagine trying to condense a legal case or a dense scientific paper into a short, understandable summary. That's the challenge we're exploring!

Now, the researchers behind this paper were curious about something specific: Do these LLMs actually grasp the key arguments within these documents? It's not enough to just parrot back facts; a good summary needs to understand why those facts matter and how they support the main point.

To figure this out, they created something called Argument Representation Coverage (ARC). Think of ARC as a measuring stick. It helps them gauge how much of the important argumentative information is retained in the summaries generated by LLMs. They focused on "argument roles," which are the different functions that parts of an argument play – things like the claim, the evidence, the reasoning, and so on. It's like understanding the different roles played by members of a sports team to win a game.

"We investigate whether instruction-tuned large language models (LLMs) adequately preserve this information."

They put three open-source LLMs to the test using two types of documents where arguments are super important: long legal opinions and scientific articles. These are documents where understanding the core arguments is absolutely critical.

So, what did they find? Well, the results were…mixed. The LLMs did okay, but they definitely weren't perfect. They managed to pick up some of the key arguments, but often missed crucial information. Imagine trying to bake a cake but forgetting the baking powder – you'll get something that looks like a cake, but it won't quite rise to the occasion. Same thing here: the summaries touched on the important points, but often lacked the depth and nuance needed to fully capture the argument.

• Key Finding: LLMs struggle to consistently cover all the salient arguments in complex documents.
• Key Finding: Critical information is often omitted, especially when the arguments are spread out.

One interesting thing they discovered was that the LLMs seemed to be influenced by where information appeared in the document. Think of it like this: LLMs have a limited "attention span," like trying to remember everything someone said in a long conversation. They might remember the beginning and end better than the middle. This positional bias affected which arguments got included in the summaries.

They also found that LLMs had certain "preferences" for different types of arguments. It's like how some people prefer chocolate ice cream over vanilla. These preferences also impacted what got included in the summaries.

So, why does this matter? Well, for lawyers, researchers, and anyone who needs to quickly understand complex information, this research highlights the limitations of relying solely on AI-generated summaries. It's a reminder that these tools are powerful, but they're not perfect. We need to be aware of their biases and limitations.

It also points the way forward for developing better AI summarization techniques. We need to create LLMs that are more argument-aware, that can better understand the structure and flow of arguments, and that are less susceptible to positional bias.

• For researchers: This work provides a valuable framework (ARC) for evaluating and improving LLM summarization.
• For lawyers and other professionals: This research highlights the need for critical evaluation of AI-generated summaries.
• For the general public: This helps u